The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing.
For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained.
Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size.
It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.
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This study was conducted to identify the protective factors that influence suicide probability in religious male high school students.
The data was collected from Nov. 5 to Dec. 10, 2009. Data were collected by self-report questionnaire from 255 students selected from 2 religious male high schools in B city. The instruments for this study were the Suicide Probability Scale for Adolescence (SPS-A), Inventory Parents Peer Attachment-Revision (IPPA-R), Spiritual Well-being Scale (SWBS), and Ego-identity Scale. The data were analyzed using t-test, one-way ANOVA, Scheffe test, Pearson correlation coefficients and stepwise multiple regression with the SPSS 14.0 program.
The protective factors of suicide probability in religious male high school students were identified as existential spiritual well-being (β= -.46,
The results suggest that improvement in spirituality, ego-identity, and mother attachment for religious male high school students is important to reduce the probability of suicide.
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Cost-benefit analysis is one of the most commonly used economic evaluation methods, which helps to inform the economic value of a program to decision makers. However, the selection of a correct benefit estimation method remains critical for accurate cost-benefit analysis. This paper compared benefit estimations among three different benefit estimation models.
Data from community-based chronic hypertension management programs in a city in South Korea were used. Three different benefit estimation methods were compared. The first was a standard deterministic estimation model; second, a repeated-measures deterministic estimation model; and third, a transitional probability estimation model.
The estimated net benefit of the three different methods were $1,273.01, $-3,749.42, and $-5,122.55 respectively.
The transitional probability estimation model showed the most correct and realistic benefit estimation, as it traced possible paths of changing status between time points and it accounted for both positive and negative benefits.
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